Diagnosing the root causes of failures in Retrieval-Augmented Generation (RAG) is challenging, as current evaluations often conflate logical reasoning errors with the failure to adhere to retrieved context. To address this, we propose **ReFa**, a framework inspired by Structural Causal Models to explicitly disentangle *Reasoning* from *Faithfulness*. Through this lens, we characterize two distinct failure modes: the **‘Fool’**, representing a deficiency in logical reasoning, and the **‘Lazy’**, representing a lapse in faithfulness where the model defaults to parametric priors. To operationalize this diagnosis, we introduce Dual Reasoning Chain Editing, a mechanism that constructs controlled proxy chains to isolate reasoning structure from evidence faithfulness. We apply this method on **ReFaBench**, a novel benchmark constructed in this work featuring factual, counterfactual, and knowledge-conflicting scenarios. Furthermore, we propose **ReFa-DPO**, a decoupled preference optimization strategy that leverages these proxies to target specific failure patterns. Experimental results demonstrate that **ReFa-DPO** enhances robustness, particularly in mitigating parametric interference, by simultaneously enhancing both contextual faithfulness and reasoning capabilities.